High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

IU-MVS					89.48 13	57.49 16		91.38 5	66.22 49	88.26 1				82.83 7	87.60 16	92.44 25

DVP-MVS		82.30 5	83.47 1	78.80 49	82.99 114	52.71 125	85.04 130	88.63 37	66.08 53	86.77 2	92.75 21	72.05 1	91.46 65	83.35 6	93.53 1	92.23 29

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 13	56.89 25	92.48 3	88.94 27	57.50 198	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

test_241102_ONE					89.48 13	56.89 25		88.94 27	57.53 196	84.61 3	93.29 13	58.81 9	96.45 1

test_241102_TWO								88.76 34	57.50 198	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 173	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 60	93.25 2	94.80 1

test0726					89.40 16	57.45 17	92.32 7	88.63 37	57.71 192	83.14 7	93.96 7	55.17 17

test_part2					89.33 18	55.48 47				82.27 8

MSP-MVS		81.30 8	81.00 10	82.20 8	89.40 16	57.45 17	92.34 5	89.99 14	57.71 192	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21

test_0728_THIRD									58.00 184	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

TSAR-MVS + MP.		78.31 25	78.26 20	78.48 60	81.33 155	56.31 37	81.59 214	86.41 76	69.61 21	81.72 11	88.16 114	55.09 19	88.04 159	74.12 59	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 19	55.08 62	88.70 48	87.92 51	55.55 225	81.21 12	93.69 9	56.51 14	94.27 21	78.36 29	85.70 36	91.51 49

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 70	60.73 4	91.65 12	86.86 68	70.30 17	80.77 13	93.07 18	37.63 177	92.28 49	82.73 9	85.71 35	91.57 46

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 54	60.97 3	91.69 11	87.02 65	70.62 13	80.75 14	93.22 14	37.77 172	92.50 42	82.75 8	86.25 31	91.57 46

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 57	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 77	55.87 42	87.58 66	86.76 70	61.48 121	80.26 16	93.10 15	46.53 77	92.41 45	79.97 21	88.77 9	92.08 33

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 79	85.46 60	49.56 196	90.99 20	86.66 73	70.58 14	80.07 17	95.30 1	56.18 15	90.97 75	82.57 10	86.22 32	93.28 10

CANet		80.90 9	81.17 9	80.09 28	87.62 33	54.21 84	91.60 13	86.47 75	73.13 5	79.89 18	93.10 15	49.88 50	92.98 31	84.09 5	84.75 47	93.08 14

HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 35	55.20 57	89.93 28	87.55 60	66.04 56	79.46 19	93.00 19	53.10 27	91.76 58	80.40 20	89.56 7	92.68 22

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 21	54.55 79	89.76 32	87.77 55	55.91 220	78.56 20	92.49 25	48.20 58	92.65 40	79.49 22	83.04 57	90.39 77

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 40	56.89 25	92.77 2	86.30 79	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 79	54.22 83	87.71 59	84.57 123	67.70 37	77.70 22	92.11 35	50.90 41	89.95 101	78.18 33	77.54 102	93.20 12

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 13	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 15	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

EPNet		78.36 23	78.49 19	77.97 77	85.49 57	52.04 141	89.36 36	84.07 134	73.22 4	77.03 25	91.72 43	49.32 54	90.17 98	73.46 66	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSFormer		73.53 90	72.19 93	77.57 85	83.02 112	55.24 54	81.63 211	81.44 177	50.28 263	76.67 26	90.91 59	44.82 101	86.11 206	60.83 144	80.09 83	91.36 54

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 112	55.24 54	91.57 14	84.82 116	69.12 23	76.67 26	92.02 36	44.82 101	90.23 96	80.83 18	80.09 83	92.08 33

alignmvs		78.08 28	77.98 25	78.39 65	83.53 95	53.22 112	89.77 31	85.45 92	66.11 51	76.59 28	91.99 38	54.07 24	89.05 119	77.34 39	77.00 107	92.89 18

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 51	85.58 54	52.40 133	88.42 52	89.03 24	60.01 140	76.06 29	92.80 20	48.34 56	92.88 33	81.66 15	86.48 29	91.04 62

CANet_DTU		73.71 88	73.14 79	75.40 132	82.61 127	50.05 187	84.67 139	79.36 211	69.72 20	75.39 30	90.03 83	29.41 255	85.93 216	67.99 94	79.11 92	90.22 81

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 83	52.68 126	86.70 87	89.02 25	59.13 164	75.37 31	92.49 25	49.06 55	93.20 29	80.67 19	87.08 21	90.71 70

VNet		77.99 30	77.92 27	78.19 70	87.43 34	50.12 186	90.93 21	91.41 4	67.48 39	75.12 32	90.15 81	46.77 74	91.00 73	73.52 65	78.46 97	93.44 7

APD-MVS		76.15 56	75.68 51	77.54 86	88.52 22	53.44 103	87.26 75	85.03 111	53.79 240	74.91 33	91.68 45	43.80 110	90.31 92	74.36 57	81.82 67	88.87 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		76.96 43	76.48 43	78.40 64	89.89 9	53.67 94	88.72 47	86.15 83	54.56 236	74.86 34	92.31 30	44.38 105	91.97 56	75.19 52	82.24 63	89.54 95

旧先验2							81.73 209		45.53 290	74.66 35			70.48 324	58.31 166

xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 62	56.34 35	89.31 37	72.84 289	61.55 116	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

SF-MVS		77.64 33	77.42 33	78.32 68	83.75 93	52.47 131	86.63 89	87.80 52	58.78 172	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 48	63.22 91	74.63 36	90.83 62	41.38 142	94.40 19	75.42 51	79.90 88	94.72 2

9.14			78.19 22		85.67 51		88.32 53	88.84 31	59.89 142	74.58 39	92.62 24	46.80 73	92.66 39	81.40 17	85.62 37

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 61	56.90 24	85.75 107	82.45 163	56.79 209	74.48 40	91.81 39	43.72 114	90.75 80	74.61 56	78.65 95	92.91 17
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + GP.		77.82 31	77.59 30	78.49 59	85.25 66	50.27 184	90.02 25	90.57 9	56.58 214	74.26 41	91.60 46	54.26 21	92.16 51	75.87 45	79.91 87	93.05 15

jason		77.01 42	76.45 44	78.69 54	79.69 176	54.74 71	90.56 23	83.99 137	68.26 28	74.10 42	90.91 59	42.14 131	89.99 100	79.30 24	79.12 91	91.36 54
jason: jason.
NCCC		79.57 16	79.23 16	80.59 18	89.50 11	56.99 23	91.38 15	88.17 47	67.71 36	73.81 43	92.75 21	46.88 72	93.28 28	78.79 26	84.07 53	91.50 50

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 64	50.69 168	88.57 50	88.59 41	58.14 181	73.60 44	93.31 12	43.14 123	93.79 25	73.81 61	88.53 11	92.37 27

CS-MVS		78.19 26	77.97 26	78.82 47	83.52 96	53.08 117	89.10 41	86.30 79	68.01 32	73.57 45	91.26 52	47.28 68	92.35 47	78.21 30	84.51 50	91.05 61

test_prior3		77.59 34	77.33 34	78.39 65	86.35 43	54.91 68	89.04 42	85.45 92	61.88 111	73.55 46	91.46 50	48.01 61	89.70 107	74.73 54	85.46 38	90.55 72

test_prior2							89.04 42		61.88 111	73.55 46	91.46 50	48.01 61		74.73 54	85.46 38

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 28	88.51 43	64.83 70	73.52 48	88.09 115	48.07 59	92.19 50	62.24 133	84.53 49	91.53 48

MVS_Test		75.85 61	74.93 63	78.62 57	84.08 85	55.20 57	83.99 154	85.17 106	68.07 30	73.38 49	82.76 178	50.44 45	89.00 123	65.90 109	80.61 76	91.64 42

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 135	57.42 19	85.21 120	76.71 257	60.68 133	73.32 50	89.34 93	47.30 67	91.63 60	68.28 92	79.72 89	91.42 51

ETV-MVS		77.17 39	76.74 40	78.48 60	81.80 137	54.55 79	86.13 98	85.33 98	68.20 29	73.10 51	90.52 70	45.23 94	90.66 82	79.37 23	80.95 71	90.22 81

ACMMP_NAP		76.43 52	75.66 52	78.73 52	81.92 135	54.67 76	84.06 151	85.35 97	61.10 126	72.99 52	91.50 48	40.25 150	91.00 73	76.84 41	86.98 22	90.51 76

TEST9					85.68 49	55.42 48	87.59 64	84.00 135	57.72 191	72.99 52	90.98 55	44.87 99	88.58 136

train_agg		76.91 44	76.40 45	78.45 62	85.68 49	55.42 48	87.59 64	84.00 135	57.84 189	72.99 52	90.98 55	44.99 96	88.58 136	78.19 31	85.32 41	91.34 56

test_8					85.72 48	55.31 52	87.60 61	83.88 138	57.84 189	72.84 55	90.99 54	44.99 96	88.34 146

xiu_mvs_v1_base_debu		71.60 116	70.29 117	75.55 129	77.26 217	53.15 113	85.34 116	79.37 208	55.83 221	72.54 56	90.19 78	22.38 297	86.66 193	73.28 67	76.39 111	86.85 148

xiu_mvs_v1_base		71.60 116	70.29 117	75.55 129	77.26 217	53.15 113	85.34 116	79.37 208	55.83 221	72.54 56	90.19 78	22.38 297	86.66 193	73.28 67	76.39 111	86.85 148

xiu_mvs_v1_base_debi		71.60 116	70.29 117	75.55 129	77.26 217	53.15 113	85.34 116	79.37 208	55.83 221	72.54 56	90.19 78	22.38 297	86.66 193	73.28 67	76.39 111	86.85 148

agg_prior1		76.68 50	76.24 48	78.00 75	85.64 52	54.92 66	87.55 67	83.61 144	57.99 185	72.53 59	91.05 53	45.36 92	88.10 157	77.76 37	84.68 48	90.99 65

agg_prior					85.64 52	54.92 66		83.61 144		72.53 59			88.10 157

Regformer-1		77.80 32	77.44 32	78.88 44	87.78 31	52.44 132	87.60 61	90.08 12	68.86 24	72.49 61	91.79 40	47.69 65	94.90 16	73.57 64	77.05 104	89.31 98

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 82	53.33 109	91.13 19	85.88 87	65.33 64	72.37 62	89.34 93	32.52 233	92.76 37	77.90 36	75.96 115	92.22 31

WTY-MVS		77.47 36	77.52 31	77.30 92	88.33 25	46.25 253	88.46 51	90.32 10	71.40 10	72.32 63	91.72 43	53.44 25	92.37 46	66.28 106	75.42 121	93.28 10

test12				79.24 36	86.89 39	56.08 40		85.16 107		72.27 64		47.15 70	91.10 71		85.93 33	90.54 75

Regformer-2		77.15 40	76.82 39	78.14 71	87.78 31	51.84 147	87.60 61	89.12 21	67.23 40	71.93 65	91.79 40	46.03 83	93.53 27	72.85 70	77.05 104	89.05 107

CSCG		80.41 12	79.72 12	82.49 5	89.12 20	57.67 14	89.29 39	91.54 3	59.19 159	71.82 66	90.05 82	59.72 6	96.04 7	78.37 28	88.40 12	93.75 5

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 158	55.31 52	89.76 32	86.91 67	62.94 95	71.65 67	91.56 47	42.33 127	92.56 41	77.14 40	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		75.11 72	74.65 67	76.46 112	78.52 198	53.35 107	83.28 176	79.94 197	70.51 15	71.64 68	88.72 104	46.02 84	86.08 210	77.52 38	75.75 119	89.96 87

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 171	54.66 77	87.06 78	85.88 87	72.11 8	71.57 69	88.63 108	50.89 43	90.35 90	76.00 44	79.11 92	91.63 43

baseline		76.86 47	76.24 48	78.71 53	80.47 170	54.20 86	83.90 156	84.88 115	71.38 11	71.51 70	89.15 98	50.51 44	90.55 86	75.71 46	78.65 95	91.39 52

testdata				67.08 266	77.59 211	45.46 260		69.20 309	44.47 297	71.50 71	88.34 109	31.21 246	70.76 323	52.20 214	75.88 116	85.03 178

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 22	55.12 59	89.95 27	85.98 86	68.31 27	71.33 72	92.75 21	45.52 91	90.37 89	71.15 77	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDDNet		74.37 78	72.13 94	81.09 17	79.58 177	56.52 31	90.02 25	86.70 72	52.61 249	71.23 73	87.20 127	31.75 243	93.96 23	74.30 58	75.77 118	92.79 20

test_yl		75.85 61	74.83 65	78.91 42	88.08 29	51.94 143	91.30 16	89.28 18	57.91 186	71.19 74	89.20 96	42.03 134	92.77 35	69.41 84	75.07 126	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 29	51.94 143	91.30 16	89.28 18	57.91 186	71.19 74	89.20 96	42.03 134	92.77 35	69.41 84	75.07 126	92.01 36

CDPH-MVS		76.05 58	75.19 58	78.62 57	86.51 42	54.98 65	87.32 70	84.59 122	58.62 175	70.75 76	90.85 61	43.10 125	90.63 84	70.50 80	84.51 50	90.24 80

HY-MVS	67.03 5	73.90 84	73.14 79	76.18 118	84.70 74	47.36 237	75.56 263	86.36 78	66.27 48	70.66 77	83.91 161	51.05 39	89.31 114	67.10 99	72.61 146	91.88 39

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 75	89.14 40	89.11 22	62.95 94	70.54 78	92.33 29	41.05 143	94.95 15	57.90 173	86.55 28	91.00 64
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CostFormer		73.89 85	72.30 90	78.66 56	82.36 131	56.58 28	75.56 263	85.30 100	66.06 54	70.50 79	76.88 242	57.02 12	89.06 117	68.27 93	68.74 171	90.33 79

PAPM		76.76 48	76.07 50	78.81 48	80.20 172	59.11 7	86.86 85	86.23 81	68.60 25	70.18 80	88.84 103	51.57 35	87.16 180	65.48 113	86.68 26	90.15 84

Regformer-3		76.02 59	75.47 55	77.70 82	85.49 57	51.47 156	85.12 126	90.19 11	68.52 26	69.36 81	90.66 64	46.45 78	94.81 17	70.25 82	73.16 137	86.81 151

MP-MVS-pluss		75.54 66	75.03 60	77.04 98	81.37 154	52.65 128	84.34 143	84.46 124	61.16 124	69.14 82	91.76 42	39.98 156	88.99 125	78.19 31	84.89 46	89.48 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PVSNet_BlendedMVS		73.42 91	73.30 78	73.76 168	85.91 46	51.83 148	86.18 97	84.24 131	65.40 61	69.09 83	80.86 203	46.70 75	88.13 155	75.43 49	65.92 191	81.33 238

PVSNet_Blended		76.53 51	76.54 41	76.50 110	85.91 46	51.83 148	88.89 45	84.24 131	67.82 34	69.09 83	89.33 95	46.70 75	88.13 155	75.43 49	81.48 70	89.55 94

GG-mvs-BLEND				77.77 80	86.68 41	50.61 169	68.67 303	88.45 44		68.73 85	87.45 126	59.15 8	90.67 81	54.83 192	87.67 15	92.03 35

EIA-MVS		75.92 60	75.18 59	78.13 72	85.14 67	51.60 152	87.17 76	85.32 99	64.69 71	68.56 86	90.53 69	45.79 88	91.58 61	67.21 98	82.18 65	91.20 57

Regformer-4		75.06 73	74.59 68	76.47 111	85.49 57	50.33 180	85.12 126	88.61 39	66.42 45	68.48 87	90.66 64	44.15 106	92.68 38	69.24 86	73.16 137	86.39 158

zzz-MVS		74.15 82	73.11 82	77.27 94	81.54 147	53.57 96	84.02 153	81.31 179	59.41 151	68.39 88	90.96 57	36.07 201	89.01 121	73.80 62	82.45 61	89.23 100

MTAPA		72.73 99	71.22 107	77.27 94	81.54 147	53.57 96	67.06 306	81.31 179	59.41 151	68.39 88	90.96 57	36.07 201	89.01 121	73.80 62	82.45 61	89.23 100

PMMVS		72.98 95	72.05 97	75.78 127	83.57 94	48.60 215	84.08 149	82.85 158	61.62 115	68.24 90	90.33 75	28.35 258	87.78 167	72.71 71	76.69 109	90.95 66

tpm2		70.82 126	68.44 141	77.98 76	80.78 162	56.11 39	74.21 273	81.28 182	60.24 138	68.04 91	75.27 260	52.26 32	88.50 141	55.82 189	68.03 175	89.33 97

DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 164	55.02 63	86.39 92	86.71 71	66.96 43	67.91 92	89.97 84	48.03 60	91.41 66	75.60 48	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		75.82 64	75.02 61	78.23 69	83.88 91	53.80 90	86.91 84	86.05 85	59.71 144	67.85 93	90.55 68	42.23 129	91.02 72	72.66 72	85.29 42	89.87 89

PAPR		75.20 70	74.13 71	78.41 63	88.31 26	55.10 61	84.31 144	85.66 90	63.76 82	67.55 94	90.73 63	43.48 119	89.40 113	66.36 105	77.03 106	90.73 69

TESTMET0.1,1		72.86 97	72.33 88	74.46 149	81.98 134	50.77 166	85.13 123	85.47 91	66.09 52	67.30 95	83.69 166	37.27 186	83.57 244	65.06 121	78.97 94	89.05 107

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 36	57.05 22	82.86 185	87.96 50	72.59 6	67.29 96	87.79 120	51.61 34	91.52 63	54.75 195	72.63 145	92.29 28

MP-MVS		74.99 74	74.33 70	76.95 103	82.89 118	53.05 119	85.63 111	83.50 146	57.86 188	67.25 97	90.24 76	43.38 120	88.85 131	76.03 43	82.23 64	88.96 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
nrg030		72.27 110	71.56 101	74.42 151	75.93 234	50.60 170	86.97 80	83.21 151	62.75 97	67.15 98	84.38 156	50.07 47	86.66 193	71.19 76	62.37 220	85.99 162

原ACMM1				76.13 119	84.89 72	54.59 78		85.26 103	51.98 253	66.70 99	87.07 131	40.15 153	89.70 107	51.23 217	85.06 45	84.10 189

ab-mvs		70.65 129	69.11 135	75.29 135	80.87 161	46.23 254	73.48 276	85.24 105	59.99 141	66.65 100	80.94 202	43.13 124	88.69 132	63.58 126	68.07 174	90.95 66

gg-mvs-nofinetune		67.43 186	64.53 209	76.13 119	85.95 45	47.79 234	64.38 310	88.28 46	39.34 313	66.62 101	41.27 334	58.69 11	89.00 123	49.64 226	86.62 27	91.59 44

UA-Net		67.32 190	66.23 178	70.59 228	78.85 188	41.23 296	73.60 275	75.45 269	61.54 118	66.61 102	84.53 155	38.73 165	86.57 198	42.48 265	74.24 130	83.98 195

sss		70.49 131	70.13 121	71.58 214	81.59 144	39.02 303	80.78 227	84.71 120	59.34 154	66.61 102	88.09 115	37.17 188	85.52 219	61.82 138	71.02 156	90.20 83

SR-MVS		70.92 125	69.73 126	74.50 148	83.38 101	50.48 174	84.27 145	79.35 212	48.96 272	66.57 104	90.45 71	33.65 224	87.11 181	66.42 103	74.56 129	85.91 165

GST-MVS		74.87 75	73.90 74	77.77 80	83.30 102	53.45 102	85.75 107	85.29 101	59.22 158	66.50 105	89.85 86	40.94 144	90.76 79	70.94 78	83.35 56	89.10 106

MSLP-MVS++		74.21 80	72.25 91	80.11 27	81.45 152	56.47 32	86.32 94	79.65 205	58.19 180	66.36 106	92.29 31	36.11 200	90.66 82	67.39 96	82.49 60	93.18 13

1121		68.79 162	66.77 169	74.82 143	83.08 109	53.46 100	80.23 234	71.53 299	45.47 291	66.31 107	87.19 128	34.02 218	85.13 228	52.78 208	80.36 80	85.87 167

Anonymous202405211		70.11 135	67.88 149	76.79 108	87.20 38	47.24 240	89.49 34	77.38 248	54.88 232	66.14 108	86.84 133	20.93 307	91.54 62	56.45 186	71.62 151	91.59 44

新几何1				73.30 178	83.10 106	53.48 99		71.43 300	45.55 289	66.14 108	87.17 129	33.88 222	80.54 265	48.50 233	80.33 81	85.88 166

MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 64	56.47 32	88.15 54	84.97 112	65.15 68	66.06 110	89.88 85	43.79 111	92.16 51	75.03 53	80.03 86	89.64 93

Fast-Effi-MVS+		72.73 99	71.15 110	77.48 87	82.75 122	54.76 70	86.77 86	80.64 189	63.05 93	65.93 111	84.01 159	44.42 104	89.03 120	56.45 186	76.36 114	88.64 116

EI-MVSNet-Vis-set		73.19 94	72.60 84	74.99 141	82.56 128	49.80 192	82.55 191	89.00 26	66.17 50	65.89 112	88.98 99	43.83 109	92.29 48	65.38 120	69.01 169	82.87 217

HFP-MVS		74.37 78	73.13 81	78.10 73	84.30 79	53.68 92	85.58 112	84.36 125	56.82 207	65.78 113	90.56 66	40.70 148	90.90 76	69.18 87	80.88 72	89.71 90

#test#		74.86 76	73.78 76	78.10 73	84.30 79	53.68 92	86.95 81	84.36 125	59.00 168	65.78 113	90.56 66	40.70 148	90.90 76	71.48 75	80.88 72	89.71 90

API-MVS		74.17 81	72.07 96	80.49 19	90.02 8	58.55 9	87.30 72	84.27 128	57.51 197	65.77 115	87.77 122	41.61 140	95.97 8	51.71 215	82.63 59	86.94 145

UGNet		68.71 164	67.11 166	73.50 175	80.55 169	47.61 235	84.08 149	78.51 229	59.45 149	65.68 116	82.73 181	23.78 289	85.08 230	52.80 207	76.40 110	87.80 132
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Vis-MVSNet		70.61 130	69.34 131	74.42 151	80.95 160	48.49 220	86.03 101	77.51 245	58.74 173	65.55 117	87.78 121	34.37 215	85.95 215	52.53 213	80.61 76	88.80 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 44	75.48 54	81.23 16	84.56 75	55.21 56	80.23 234	91.64 2	58.65 174	65.37 118	91.48 49	45.72 89	95.05 14	72.11 74	89.52 8	93.44 7

ACMMPR		73.76 86	72.61 83	77.24 97	83.92 89	52.96 122	85.58 112	84.29 127	56.82 207	65.12 119	90.45 71	37.24 187	90.18 97	69.18 87	80.84 74	88.58 118

region2R		73.75 87	72.55 85	77.33 91	83.90 90	52.98 121	85.54 115	84.09 133	56.83 206	65.10 120	90.45 71	37.34 185	90.24 95	68.89 89	80.83 75	88.77 114

EI-MVSNet-UG-set		72.37 105	71.73 99	74.29 154	81.60 143	49.29 202	81.85 206	88.64 36	65.29 66	65.05 121	88.29 111	43.18 121	91.83 57	63.74 125	67.97 176	81.75 226

VPA-MVSNet		71.12 120	70.66 112	72.49 192	78.75 190	44.43 270	87.64 60	90.02 13	63.97 78	65.02 122	81.58 197	42.14 131	87.42 176	63.42 127	63.38 208	85.63 172

CLD-MVS		75.60 65	75.39 56	76.24 115	80.69 165	52.40 133	90.69 22	86.20 82	74.40 3	65.01 123	88.93 100	42.05 133	90.58 85	76.57 42	73.96 132	85.73 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmrst		71.04 122	69.77 125	74.86 142	83.19 105	55.86 43	75.64 262	78.73 224	67.88 33	64.99 124	73.73 268	49.96 49	79.56 279	65.92 108	67.85 178	89.14 105

CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 108	62.84 2	85.73 109	85.39 95	69.79 19	64.87 125	83.49 168	41.52 141	93.69 26	70.55 79	81.82 67	92.12 32

VPNet		72.07 111	71.42 105	74.04 159	78.64 195	47.17 241	89.91 30	87.97 49	72.56 7	64.66 126	85.04 152	41.83 138	88.33 148	61.17 141	60.97 226	86.62 153

baseline1		72.51 104	72.12 95	73.69 169	85.05 68	44.46 268	83.51 166	86.13 84	71.61 9	64.64 127	87.97 118	55.00 20	89.48 112	59.07 157	56.05 268	87.13 144

TR-MVS		69.71 145	67.85 151	75.27 136	82.94 116	48.48 221	87.40 69	80.86 186	57.15 203	64.61 128	87.08 130	32.67 232	89.64 110	46.38 246	71.55 153	87.68 135

PVSNet_Blended_VisFu		73.40 92	72.44 87	76.30 113	81.32 156	54.70 74	85.81 103	78.82 221	63.70 83	64.53 129	85.38 149	47.11 71	87.38 177	67.75 95	77.55 101	86.81 151

HQP-NCC					79.02 184		88.00 55		65.45 58	64.48 130

ACMP_Plane					79.02 184		88.00 55		65.45 58	64.48 130

HQP-MVS		72.34 106	71.44 104	75.03 139	79.02 184	51.56 153	88.00 55	83.68 141	65.45 58	64.48 130	85.13 150	37.35 183	88.62 134	66.70 101	73.12 139	84.91 181

EPNet_dtu		66.25 209	66.71 171	64.87 282	78.66 194	34.12 317	82.80 186	75.51 267	61.75 113	64.47 133	86.90 132	37.06 189	72.46 319	43.65 258	69.63 167	88.02 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP4-MVS										64.47 133			88.61 135			84.91 181

APD-MVS_3200maxsize		69.62 149	68.23 144	73.80 167	81.58 145	48.22 227	81.91 204	79.50 207	48.21 274	64.24 135	89.75 88	31.91 242	87.55 173	63.08 128	73.85 134	85.64 171

thisisatest0515		73.64 89	72.20 92	77.97 77	81.63 141	53.01 120	86.69 88	88.81 32	62.53 100	64.06 136	85.65 145	52.15 33	92.50 42	58.43 162	69.84 164	88.39 122

Anonymous20240529		69.71 145	67.28 163	77.00 101	83.78 92	50.36 178	88.87 46	85.10 110	47.22 279	64.03 137	83.37 170	27.93 263	92.10 54	57.78 175	67.44 179	88.53 120

HPM-MVS		72.60 101	71.50 102	75.89 125	82.02 133	51.42 158	80.70 228	83.05 154	56.12 219	64.03 137	89.53 91	37.55 179	88.37 144	70.48 81	80.04 85	87.88 130
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVS		72.92 96	71.62 100	76.81 105	83.41 97	52.48 129	84.88 135	83.20 152	58.03 182	63.91 139	89.63 90	35.50 207	89.78 104	65.50 111	80.50 78	88.16 123

X-MVStestdata		65.85 214	62.20 221	76.81 105	83.41 97	52.48 129	84.88 135	83.20 152	58.03 182	63.91 139	4.82 349	35.50 207	89.78 104	65.50 111	80.50 78	88.16 123

EI-MVSNet		69.70 147	68.70 138	72.68 187	75.00 245	48.90 210	79.54 242	87.16 63	61.05 127	63.88 141	83.74 164	45.87 86	90.44 87	57.42 179	64.68 197	78.70 263

MVSTER		73.25 93	72.33 88	76.01 123	85.54 56	53.76 91	83.52 162	87.16 63	67.06 42	63.88 141	81.66 196	52.77 28	90.44 87	64.66 122	64.69 196	83.84 200

abl_6		68.03 173	66.15 180	73.66 170	78.54 197	48.48 221	79.77 239	78.04 235	47.39 278	63.70 143	88.25 112	28.21 259	89.06 117	60.17 154	71.25 154	83.45 205

CP-MVS		72.59 103	71.46 103	76.00 124	82.93 117	52.32 138	86.93 83	82.48 162	55.15 229	63.65 144	90.44 74	35.03 211	88.53 140	68.69 90	77.83 100	87.15 143

BH-RMVSNet		70.08 137	68.01 146	76.27 114	84.21 83	51.22 164	87.29 73	79.33 214	58.96 170	63.63 145	86.77 134	33.29 227	90.30 94	44.63 254	73.96 132	87.30 142

DP-MVS Recon		71.99 112	70.31 116	77.01 100	90.65 6	53.44 103	89.37 35	82.97 156	56.33 217	63.56 146	89.47 92	34.02 218	92.15 53	54.05 198	72.41 147	85.43 175

tpm		68.36 168	67.48 160	70.97 224	79.93 174	51.34 160	76.58 259	78.75 223	67.73 35	63.54 147	74.86 262	48.33 57	72.36 320	53.93 199	63.71 203	89.21 102

PGM-MVS		72.60 101	71.20 108	76.80 107	82.95 115	52.82 124	83.07 180	82.14 164	56.51 215	63.18 148	89.81 87	35.68 206	89.76 106	67.30 97	80.19 82	87.83 131

test-LLR		69.65 148	69.01 136	71.60 212	78.67 192	48.17 228	85.13 123	79.72 202	59.18 161	63.13 149	82.58 183	36.91 192	80.24 270	60.56 148	75.17 123	86.39 158

test-mter		68.36 168	67.29 162	71.60 212	78.67 192	48.17 228	85.13 123	79.72 202	53.38 243	63.13 149	82.58 183	27.23 269	80.24 270	60.56 148	75.17 123	86.39 158

OPM-MVS		70.75 128	69.58 127	74.26 155	75.55 239	51.34 160	86.05 100	83.29 150	61.94 110	62.95 151	85.77 144	34.15 217	88.44 142	65.44 117	71.07 155	82.99 214

test222					79.36 178	50.97 165	77.99 251	67.84 311	42.54 308	62.84 152	86.53 138	30.26 251			76.91 108	85.23 176

XXY-MVS		70.18 134	69.28 134	72.89 184	77.64 210	42.88 282	85.06 129	87.50 61	62.58 99	62.66 153	82.34 188	43.64 116	89.83 103	58.42 164	63.70 204	85.96 164

CDS-MVSNet		70.48 132	69.43 128	73.64 171	77.56 212	48.83 212	83.51 166	77.45 246	63.27 90	62.33 154	85.54 148	43.85 108	83.29 248	57.38 180	74.00 131	88.79 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP_MVS		70.96 124	69.91 124	74.12 157	77.95 206	49.57 194	85.76 105	82.59 160	63.60 86	62.15 155	83.28 172	36.04 203	88.30 150	65.46 114	72.34 148	84.49 184

plane_prior3						48.95 207			64.01 77	62.15 155

mvs-test1		69.04 154	67.57 157	73.44 176	75.17 240	51.68 151	86.57 91	74.48 275	62.15 104	62.07 157	85.79 143	30.59 249	87.48 174	65.40 118	65.94 190	81.18 242

mPP-MVS		71.79 115	70.38 115	76.04 122	82.65 126	52.06 140	84.45 141	81.78 172	55.59 224	62.05 158	89.68 89	33.48 225	88.28 152	65.45 116	78.24 99	87.77 133

PCF-MVS	61.03 10	70.10 136	68.40 142	75.22 138	77.15 221	51.99 142	79.30 245	82.12 165	56.47 216	61.88 159	86.48 140	43.98 107	87.24 179	55.37 190	72.79 144	86.43 157
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs_anonymous		72.29 108	70.74 111	76.94 104	82.85 119	54.72 73	78.43 250	81.54 175	63.77 81	61.69 160	79.32 213	51.11 38	85.31 223	62.15 135	75.79 117	90.79 68

IB-MVS	68.87 2	74.01 83	72.03 98	79.94 29	83.04 111	55.50 46	90.24 24	88.65 35	67.14 41	61.38 161	81.74 195	53.21 26	94.28 20	60.45 150	62.41 219	90.03 86
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
RRT_test8_iter05		72.74 98	71.20 108	77.36 90	87.25 37	53.51 98	88.68 49	89.53 16	65.20 67	61.32 162	81.27 199	45.89 85	92.48 44	65.99 107	55.65 274	86.10 161

TAMVS		69.51 151	68.16 145	73.56 174	76.30 228	48.71 214	82.57 189	77.17 251	62.10 106	61.32 162	84.23 157	41.90 136	83.46 246	54.80 194	73.09 141	88.50 121

1112_ss		70.05 138	69.37 130	72.10 197	80.77 163	42.78 283	85.12 126	76.75 256	59.69 145	61.19 164	92.12 33	47.48 66	83.84 240	53.04 204	68.21 173	89.66 92

EPMVS		68.45 167	65.44 198	77.47 88	84.91 71	56.17 38	71.89 291	81.91 170	61.72 114	60.85 165	72.49 281	36.21 199	87.06 183	47.32 240	71.62 151	89.17 104

v2v482		69.55 150	67.64 154	75.26 137	72.32 274	53.83 89	84.93 134	81.94 167	65.37 63	60.80 166	79.25 214	41.62 139	88.98 126	63.03 129	59.51 233	82.98 215

MVS_111021_LR		69.07 153	67.91 147	72.54 190	77.27 216	49.56 196	79.77 239	73.96 281	59.33 156	60.73 167	87.82 119	30.19 252	81.53 256	69.94 83	72.19 150	86.53 154

GA-MVS		69.04 154	66.70 172	76.06 121	75.11 242	52.36 136	83.12 178	80.23 193	63.32 89	60.65 168	79.22 215	30.98 247	88.37 144	61.25 140	66.41 185	87.46 138

RRT_MVS		65.43 217	64.01 214	69.68 242	81.54 147	50.15 185	82.31 197	76.78 255	55.25 228	60.64 169	82.00 193	25.18 282	79.00 280	60.96 142	51.45 294	79.89 258

PAPM_NR		71.80 114	69.98 123	77.26 96	81.54 147	53.34 108	78.60 249	85.25 104	53.46 242	60.53 170	88.66 105	45.69 90	89.24 115	56.49 183	79.62 90	89.19 103

v1144		68.81 160	66.82 167	74.80 145	72.34 273	53.46 100	84.68 138	81.77 173	64.25 75	60.28 171	77.91 224	40.23 151	88.95 127	60.37 151	59.52 232	81.97 223

thres200		68.71 164	67.27 164	73.02 180	84.73 73	46.76 244	85.03 131	87.73 56	62.34 103	59.87 172	83.45 169	43.15 122	88.32 149	31.25 302	67.91 177	83.98 195

3Dnovator	64.70 6	74.46 77	72.48 86	80.41 20	82.84 120	55.40 51	83.08 179	88.61 39	67.61 38	59.85 173	88.66 105	34.57 214	93.97 22	58.42 164	88.70 10	91.85 40

PVSNet	62.49 8	69.27 152	67.81 152	73.64 171	84.41 78	51.85 146	84.63 140	77.80 239	66.42 45	59.80 174	84.95 153	22.14 301	80.44 267	55.03 191	75.11 125	88.62 117

QAPM		71.88 113	69.33 132	79.52 32	82.20 132	54.30 82	86.30 95	88.77 33	56.61 213	59.72 175	87.48 125	33.90 221	95.36 11	47.48 239	81.49 69	88.90 110

BH-w/o		70.02 139	68.51 140	74.56 147	82.77 121	50.39 177	86.60 90	78.14 234	59.77 143	59.65 176	85.57 147	39.27 161	87.30 178	49.86 224	74.94 128	85.99 162

UniMVSNet (Re)		67.71 179	66.80 168	70.45 230	74.44 250	42.93 281	82.42 195	84.90 114	63.69 84	59.63 177	80.99 201	47.18 69	85.23 225	51.17 218	56.75 260	83.19 212

Baseline_NR-MVSNet		65.49 216	64.27 211	69.13 246	74.37 253	41.65 292	83.39 173	78.85 219	59.56 147	59.62 178	76.88 242	40.75 145	87.44 175	49.99 222	55.05 276	78.28 272

v1192		67.96 175	65.74 190	74.63 146	71.79 276	53.43 105	84.06 151	80.99 185	63.19 92	59.56 179	77.46 231	37.50 182	88.65 133	58.20 167	58.93 238	81.79 225

HPM-MVS_fast		67.86 176	66.28 177	72.61 188	80.67 166	48.34 225	81.18 220	75.95 265	50.81 262	59.55 180	88.05 117	27.86 264	85.98 212	58.83 159	73.58 135	83.51 204

V42		67.66 180	65.60 194	73.86 164	70.69 289	53.63 95	81.50 215	78.61 227	63.85 80	59.49 181	77.49 230	37.98 169	87.65 170	62.33 132	58.43 243	80.29 253

miper_enhance_ethall		69.77 144	68.90 137	72.38 193	78.93 187	49.91 190	83.29 175	78.85 219	64.90 69	59.37 182	79.46 211	52.77 28	85.16 227	63.78 124	58.72 239	82.08 222

PatchmatchNet		67.07 197	63.63 216	77.40 89	83.10 106	58.03 10	72.11 289	77.77 240	58.85 171	59.37 182	70.83 292	37.84 171	84.93 232	42.96 262	69.83 165	89.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OMC-MVS		65.97 213	65.06 204	68.71 254	72.97 265	42.58 287	78.61 248	75.35 270	54.72 233	59.31 184	86.25 141	33.30 226	77.88 291	57.99 169	67.05 181	85.66 170

MDTV_nov1_ep13_2view						43.62 277	71.13 294		54.95 231	59.29 185		36.76 194		46.33 247		87.32 141

v144192		67.86 176	65.76 189	74.16 156	71.68 278	53.09 116	84.14 148	80.83 187	62.85 96	59.21 186	77.28 234	39.30 160	88.00 160	58.67 161	57.88 254	81.40 235

v1921920		67.45 185	65.23 202	74.10 158	71.51 281	52.90 123	83.75 160	80.44 192	62.48 102	59.12 187	77.13 235	36.98 190	87.90 161	57.53 177	58.14 248	81.49 230

baseline2		75.15 71	74.54 69	76.98 102	81.67 140	51.74 150	83.84 157	91.94 1	69.97 18	58.98 188	86.02 142	59.73 5	91.73 59	68.37 91	70.40 161	87.48 137

v148		68.24 172	66.35 175	73.88 163	71.76 277	51.47 156	84.23 146	81.90 171	63.69 84	58.94 189	76.44 247	43.72 114	87.78 167	60.63 146	55.86 271	82.39 220

1314		71.11 121	69.41 129	76.22 116	79.32 180	50.49 173	80.23 234	85.14 109	59.44 150	58.93 190	88.89 102	33.83 223	89.60 111	61.49 139	77.42 103	88.57 119

cascas		69.01 156	66.13 181	77.66 83	79.36 178	55.41 50	86.99 79	83.75 140	56.69 211	58.92 191	81.35 198	24.31 287	92.10 54	53.23 201	70.61 159	85.46 174

PS-MVSNAJss		68.78 163	67.17 165	73.62 173	73.01 264	48.33 226	84.95 133	84.81 117	59.30 157	58.91 192	79.84 209	37.77 172	88.86 130	62.83 130	63.12 214	83.67 203

HyFIR lowres test		69.94 142	67.58 155	77.04 98	77.11 222	57.29 20	81.49 217	79.11 217	58.27 179	58.86 193	80.41 206	42.33 127	86.96 186	61.91 136	68.68 172	86.87 146

MDTV_nov1_ep13			61.56 226		81.68 139	55.12 59	72.41 285	78.18 233	59.19 159	58.85 194	69.29 300	34.69 213	86.16 205	36.76 281	62.96 215

ACMMP		70.81 127	69.29 133	75.39 133	81.52 151	51.92 145	83.43 169	83.03 155	56.67 212	58.80 195	88.91 101	31.92 241	88.58 136	65.89 110	73.39 136	85.67 169
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
tpm cat1		66.28 208	62.78 217	76.77 109	81.40 153	57.14 21	70.03 298	77.19 250	53.00 246	58.76 196	70.73 295	46.17 79	86.73 191	43.27 259	64.46 198	86.44 156

thisisatest0530		70.47 133	68.56 139	76.20 117	79.78 175	51.52 155	83.49 168	88.58 42	57.62 195	58.60 197	82.79 177	51.03 40	91.48 64	52.84 206	62.36 221	85.59 173

UniMVSNet_NR-MVSNet		68.82 159	68.29 143	70.40 232	75.71 237	42.59 285	84.23 146	86.78 69	66.31 47	58.51 198	82.45 185	51.57 35	84.64 236	53.11 202	55.96 269	83.96 197

DU-MVS		66.84 202	65.74 190	70.16 235	73.27 262	42.59 285	81.50 215	82.92 157	63.53 88	58.51 198	82.11 191	40.75 145	84.64 236	53.11 202	55.96 269	83.24 210

WR-MVS		67.58 181	66.76 170	70.04 239	75.92 235	45.06 266	86.23 96	85.28 102	64.31 74	58.50 200	81.00 200	44.80 103	82.00 255	49.21 229	55.57 275	83.06 213

3Dnovator+	62.71 7	72.29 108	70.50 113	77.65 84	83.40 100	51.29 162	87.32 70	86.40 77	59.01 167	58.49 201	88.32 110	32.40 234	91.27 69	57.04 181	82.15 66	90.38 78

cl-mvsnet2		68.85 157	67.69 153	72.35 194	78.07 205	49.98 189	82.45 194	78.48 230	62.50 101	58.46 202	77.95 223	49.99 48	85.17 226	62.55 131	58.72 239	81.90 224

Test_1112_low_res		67.18 193	66.23 178	70.02 240	78.75 190	41.02 297	83.43 169	73.69 283	57.29 201	58.45 203	82.39 187	45.30 93	80.88 262	50.50 220	66.26 189	88.16 123

v1240		66.99 198	64.68 207	73.93 161	71.38 284	52.66 127	83.39 173	79.98 196	61.97 109	58.44 204	77.11 236	35.25 209	87.81 163	56.46 185	58.15 246	81.33 238

TranMVSNet+NR-MVSNet		66.94 200	65.61 193	70.93 225	73.45 259	43.38 279	83.02 182	84.25 129	65.31 65	58.33 205	81.90 194	39.92 157	85.52 219	49.43 227	54.89 278	83.89 199

miper_ehance_all_eth		68.70 166	67.58 155	72.08 198	76.91 223	49.48 199	82.47 193	78.45 231	62.68 98	58.28 206	77.88 225	50.90 41	85.01 231	61.91 136	58.72 239	81.75 226

EPP-MVSNet		71.14 119	70.07 122	74.33 153	79.18 183	46.52 247	83.81 158	86.49 74	56.32 218	57.95 207	84.90 154	54.23 22	89.14 116	58.14 168	69.65 166	87.33 140

CPTT-MVS		67.15 194	65.84 187	71.07 222	80.96 159	50.32 181	81.94 203	74.10 278	46.18 287	57.91 208	87.64 124	29.57 254	81.31 258	64.10 123	70.18 163	81.56 229

Effi-MVS+-dtu		66.24 210	64.96 206	70.08 237	75.17 240	49.64 193	82.01 201	74.48 275	62.15 104	57.83 209	76.08 255	30.59 249	83.79 241	65.40 118	60.93 227	76.81 285

AdaColmap		67.86 176	65.48 195	75.00 140	88.15 28	54.99 64	86.10 99	76.63 259	49.30 269	57.80 210	86.65 137	29.39 256	88.94 129	45.10 252	70.21 162	81.06 243

tfpn200view9		67.57 182	66.13 181	71.89 210	84.05 86	45.07 263	83.40 171	87.71 58	60.79 130	57.79 211	82.76 178	43.53 117	87.80 164	28.80 308	66.36 186	82.78 218

thres400		67.40 189	66.13 181	71.19 220	84.05 86	45.07 263	83.40 171	87.71 58	60.79 130	57.79 211	82.76 178	43.53 117	87.80 164	28.80 308	66.36 186	80.71 248

SCA		63.84 223	60.01 238	75.32 134	78.58 196	57.92 11	61.61 318	77.53 244	56.71 210	57.75 213	70.77 293	31.97 239	79.91 276	48.80 230	56.36 261	88.13 126

FIs		70.00 140	70.24 120	69.30 245	77.93 208	38.55 305	83.99 154	87.72 57	66.86 44	57.66 214	84.17 158	52.28 31	85.31 223	52.72 212	68.80 170	84.02 191

cl_fuxian		67.97 174	66.66 173	71.91 209	76.20 230	49.31 201	82.13 200	78.00 237	61.99 108	57.64 215	76.94 239	49.41 52	84.93 232	60.62 147	57.01 259	81.49 230

GBi-Net		67.09 195	65.47 196	71.96 203	82.71 123	46.36 249	83.52 162	83.31 147	58.55 176	57.58 216	76.23 251	36.72 195	86.20 202	47.25 241	63.40 205	83.32 207

test1		67.09 195	65.47 196	71.96 203	82.71 123	46.36 249	83.52 162	83.31 147	58.55 176	57.58 216	76.23 251	36.72 195	86.20 202	47.25 241	63.40 205	83.32 207

FMVSNet3		68.84 158	67.40 161	73.19 179	85.05 68	48.53 218	85.71 110	85.36 96	60.90 129	57.58 216	79.15 216	42.16 130	86.77 189	47.25 241	63.40 205	84.27 188

CR-MVSNet		62.47 237	59.04 246	72.77 185	73.97 257	56.57 29	60.52 321	71.72 295	60.04 139	57.49 219	65.86 311	38.94 162	80.31 268	42.86 263	59.93 230	81.42 233

RPMNet		58.49 264	53.74 275	72.77 185	73.97 257	56.57 29	60.52 321	72.39 291	35.72 324	57.49 219	58.87 327	37.73 175	80.31 268	27.01 318	59.93 230	81.42 233

BH-untuned		68.28 171	66.40 174	73.91 162	81.62 142	50.01 188	85.56 114	77.39 247	57.63 194	57.47 221	83.69 166	36.36 198	87.08 182	44.81 253	73.08 142	84.65 183

XVG-OURS-SEG-HR		62.02 240	59.54 241	69.46 244	65.30 314	45.88 256	65.06 308	73.57 285	46.45 285	57.42 222	83.35 171	26.95 271	78.09 287	53.77 200	64.03 200	84.42 186

XVG-OURS		61.88 241	59.34 243	69.49 243	65.37 313	46.27 252	64.80 309	73.49 286	47.04 281	57.41 223	82.85 176	25.15 283	78.18 285	53.00 205	64.98 192	84.01 192

IS-MVSNet		68.80 161	67.55 158	72.54 190	78.50 199	43.43 278	81.03 222	79.35 212	59.12 165	57.27 224	86.71 135	46.05 82	87.70 169	44.32 255	75.60 120	86.49 155

TAPA-MVS	56.12 14	61.82 242	60.18 237	66.71 268	78.48 200	37.97 308	75.19 268	76.41 262	46.82 282	57.04 225	86.52 139	27.67 267	77.03 297	26.50 320	67.02 182	85.14 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
eth_miper_zixun_eth		66.98 199	65.28 201	72.06 199	75.61 238	50.40 176	81.00 223	76.97 254	62.00 107	56.99 226	76.97 238	44.84 100	85.58 218	58.75 160	54.42 281	80.21 254

cl-mvsnet1		67.43 186	65.93 185	71.94 207	76.33 226	48.01 232	82.57 189	79.11 217	61.31 122	56.73 227	76.92 240	46.09 81	86.43 201	57.98 170	56.31 263	81.39 236

cl-mvsnet_		67.43 186	65.93 185	71.95 206	76.33 226	48.02 231	82.58 188	79.12 216	61.30 123	56.72 228	76.92 240	46.12 80	86.44 200	57.98 170	56.31 263	81.38 237

FMVSNet2		67.57 182	65.79 188	72.90 182	82.71 123	47.97 233	85.15 122	84.93 113	58.55 176	56.71 229	78.26 222	36.72 195	86.67 192	46.15 248	62.94 216	84.07 190

OpenMVS	61.00 11	69.99 141	67.55 158	77.30 92	78.37 202	54.07 88	84.36 142	85.76 89	57.22 202	56.71 229	87.67 123	30.79 248	92.83 34	43.04 260	84.06 54	85.01 179

Anonymous20231211		66.08 212	63.67 215	73.31 177	83.07 110	48.75 213	86.01 102	84.67 121	45.27 292	56.54 231	76.67 245	28.06 262	88.95 127	52.78 208	59.95 229	82.23 221

MVP-Stereo		70.97 123	70.44 114	72.59 189	76.03 233	51.36 159	85.02 132	86.99 66	60.31 137	56.53 232	78.92 218	40.11 154	90.00 99	60.00 155	90.01 4	76.41 290
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		72.34 106	71.26 106	75.61 128	82.38 130	55.55 45	88.00 55	89.95 15	65.38 62	56.51 233	80.74 205	32.28 236	92.89 32	57.95 172	88.10 13	78.39 270

Fast-Effi-MVS+-dtu		66.53 205	64.10 213	73.84 165	72.41 272	52.30 139	84.73 137	75.66 266	59.51 148	56.34 234	79.11 217	28.11 261	85.85 217	57.74 176	63.29 209	83.35 206

CVMVSNet		60.85 246	60.44 235	62.07 293	75.00 245	32.73 324	79.54 242	73.49 286	36.98 321	56.28 235	83.74 164	29.28 257	69.53 326	46.48 245	63.23 210	83.94 198

thres600view7		66.46 206	65.12 203	70.47 229	83.41 97	43.80 276	82.15 199	87.78 53	59.37 153	56.02 236	82.21 189	43.73 112	86.90 187	26.51 319	64.94 193	80.71 248

thres100view900		66.87 201	65.42 199	71.24 218	83.29 103	43.15 280	81.67 210	87.78 53	59.04 166	55.92 237	82.18 190	43.73 112	87.80 164	28.80 308	66.36 186	82.78 218

IterMVS-LS		66.63 203	65.36 200	70.42 231	75.10 243	48.90 210	81.45 218	76.69 258	61.05 127	55.71 238	77.10 237	45.86 87	83.65 243	57.44 178	57.88 254	78.70 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
114514_t		69.87 143	67.88 149	75.85 126	88.38 24	52.35 137	86.94 82	83.68 141	53.70 241	55.68 239	85.60 146	30.07 253	91.20 70	55.84 188	71.02 156	83.99 193

dp		64.41 219	61.58 225	72.90 182	82.40 129	54.09 87	72.53 283	76.59 260	60.39 136	55.68 239	70.39 296	35.18 210	76.90 299	39.34 269	61.71 223	87.73 134

FC-MVSNet-test		67.49 184	67.91 147	66.21 272	76.06 231	33.06 322	80.82 226	87.18 62	64.44 73	54.81 241	82.87 175	50.40 46	82.60 250	48.05 236	66.55 184	82.98 215

UniMVSNet_ETH3D		62.51 235	60.49 234	68.57 257	68.30 304	40.88 299	73.89 274	79.93 198	51.81 257	54.77 242	79.61 210	24.80 285	81.10 259	49.93 223	61.35 224	83.73 201

tttt0517		68.33 170	66.29 176	74.46 149	78.08 204	49.06 204	80.88 225	89.08 23	54.40 238	54.75 243	80.77 204	51.31 37	90.33 91	49.35 228	58.01 250	83.99 193

MIMVSNet		63.12 230	60.29 236	71.61 211	75.92 235	46.65 245	65.15 307	81.94 167	59.14 163	54.65 244	69.47 299	25.74 278	80.63 264	41.03 266	69.56 168	87.55 136

ACMM	58.35 12	64.35 220	62.01 223	71.38 216	74.21 254	48.51 219	82.25 198	79.66 204	47.61 276	54.54 245	80.11 207	25.26 281	86.00 211	51.26 216	63.16 212	79.64 260
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		64.57 218	62.11 222	71.96 203	77.32 215	46.36 249	83.52 162	83.31 147	52.43 251	54.42 246	76.23 251	27.80 265	86.20 202	42.59 264	61.34 225	83.32 207

PatchT		56.60 272	52.97 278	67.48 262	72.94 266	46.16 255	57.30 325	73.78 282	38.77 315	54.37 247	57.26 330	37.52 180	78.06 288	32.02 297	52.79 290	78.23 274

v8		67.25 191	64.99 205	74.04 159	72.89 267	53.31 110	82.37 196	80.11 195	61.54 118	54.29 248	76.02 256	42.89 126	88.41 143	58.43 162	56.36 261	80.39 252

pmmvs4		63.34 228	61.07 230	70.16 235	70.14 291	50.53 172	79.97 238	71.41 301	55.08 230	54.12 249	78.58 220	32.79 231	82.09 254	50.33 221	57.22 258	77.86 276

v10		66.61 204	64.20 212	73.83 166	72.59 270	53.37 106	81.88 205	79.91 199	61.11 125	54.09 250	75.60 258	40.06 155	88.26 153	56.47 184	56.10 267	79.86 259

pm-mvs1		64.12 221	62.56 218	68.78 252	71.68 278	38.87 304	82.89 184	81.57 174	55.54 226	53.89 251	77.82 226	37.73 175	86.74 190	48.46 234	53.49 288	80.72 247

LPG-MVS_test		66.44 207	64.58 208	72.02 200	74.42 251	48.60 215	83.07 180	80.64 189	54.69 234	53.75 252	83.83 162	25.73 279	86.98 184	60.33 152	64.71 194	80.48 250

LGP-MVS_train				72.02 200	74.42 251	48.60 215		80.64 189	54.69 234	53.75 252	83.83 162	25.73 279	86.98 184	60.33 152	64.71 194	80.48 250

NR-MVSNet		67.25 191	65.99 184	71.04 223	73.27 262	43.91 274	85.32 119	84.75 119	66.05 55	53.65 254	82.11 191	45.05 95	85.97 214	47.55 238	56.18 266	83.24 210

tpmvs		62.45 238	59.42 242	71.53 215	83.93 88	54.32 81	70.03 298	77.61 243	51.91 254	53.48 255	68.29 303	37.91 170	86.66 193	33.36 292	58.27 244	73.62 307

ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 55	84.52 76	55.59 44	88.92 44	89.21 20	68.06 31	53.13 256	90.22 77	49.71 51	87.62 171	72.12 73	70.82 158	92.82 19

test0.0.03 1		62.54 234	62.44 219	62.86 292	72.28 275	29.51 331	82.93 183	78.78 222	59.18 161	53.07 257	82.41 186	36.91 192	77.39 295	37.45 273	58.96 237	81.66 228

miper_lstm_enhance		63.91 222	62.30 220	68.75 253	75.06 244	46.78 243	69.02 302	81.14 183	59.68 146	52.76 258	72.39 284	40.71 147	77.99 289	56.81 182	53.09 289	81.48 232

MVS_0304		56.72 270	55.17 266	61.37 300	70.71 287	36.80 313	75.74 260	68.75 310	44.11 301	52.53 259	68.20 304	15.05 328	74.53 307	42.98 261	58.44 242	72.79 313

TransMVSNet (Re)		62.82 232	60.76 232	69.02 247	73.98 256	41.61 293	86.36 93	79.30 215	56.90 204	52.53 259	76.44 247	41.85 137	87.60 172	38.83 270	40.61 321	77.86 276

ACMP	61.11 9	66.24 210	64.33 210	72.00 202	74.89 247	49.12 203	83.18 177	79.83 200	55.41 227	52.29 261	82.68 182	25.83 277	86.10 208	60.89 143	63.94 202	80.78 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		63.84 223	61.56 226	70.70 227	68.78 298	44.69 267	81.63 211	81.44 177	50.28 263	52.27 262	76.26 250	26.72 272	86.11 206	60.83 144	55.84 272	81.29 241

Vis-MVSNet (Re-imp)		65.52 215	65.63 192	65.17 280	77.49 213	30.54 329	75.49 266	77.73 241	59.34 154	52.26 263	86.69 136	49.38 53	80.53 266	37.07 277	75.28 122	84.42 186

pmmvs5		62.80 233	61.18 229	67.66 261	69.53 295	42.37 290	82.65 187	75.19 271	54.30 239	52.03 264	78.51 221	31.64 244	80.67 263	48.60 232	58.15 246	79.95 257

CNLPA		60.59 247	58.44 249	67.05 267	79.21 182	47.26 239	79.75 241	64.34 319	42.46 309	51.90 265	83.94 160	27.79 266	75.41 304	37.12 275	59.49 234	78.47 267

IterMVS		63.77 225	61.67 224	70.08 237	72.68 269	51.24 163	80.44 230	75.51 267	60.51 135	51.41 266	73.70 271	32.08 238	78.91 281	54.30 197	54.35 282	80.08 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpnnormal		61.47 243	59.09 245	68.62 256	76.29 229	41.69 291	81.14 221	85.16 107	54.48 237	51.32 267	73.63 272	32.32 235	86.89 188	21.78 329	55.71 273	77.29 282

anonymousdsp		60.46 248	57.65 251	68.88 248	63.63 322	45.09 262	72.93 281	78.63 226	46.52 284	51.12 268	72.80 280	21.46 304	83.07 249	57.79 174	53.97 283	78.47 267

ADS-MVSNet2		55.21 282	51.44 285	66.51 271	80.60 167	49.56 196	55.03 327	65.44 315	44.72 295	51.00 269	61.19 321	22.83 293	75.41 304	28.54 311	53.63 285	74.57 301

ADS-MVSNet		56.17 276	51.95 284	68.84 249	80.60 167	53.07 118	55.03 327	70.02 307	44.72 295	51.00 269	61.19 321	22.83 293	78.88 282	28.54 311	53.63 285	74.57 301

jajsoiax		63.21 229	60.84 231	70.32 233	68.33 303	44.45 269	81.23 219	81.05 184	53.37 244	50.96 271	77.81 227	17.49 319	85.49 221	59.31 156	58.05 249	81.02 244

CHOSEN 280x420		57.53 268	56.38 260	60.97 302	74.01 255	48.10 230	46.30 333	54.31 331	48.18 275	50.88 272	77.43 232	38.37 168	59.16 334	54.83 192	63.14 213	75.66 294

mvs_tets		62.96 231	60.55 233	70.19 234	68.22 306	44.24 273	80.90 224	80.74 188	52.99 247	50.82 273	77.56 228	16.74 322	85.44 222	59.04 158	57.94 251	80.89 245

IterMVS-SCA-FT		59.12 254	58.81 248	60.08 304	70.68 290	45.07 263	80.42 231	74.25 277	43.54 304	50.02 274	73.73 268	31.97 239	56.74 335	51.06 219	53.60 287	78.42 269

D2MVS		63.49 227	61.39 228	69.77 241	69.29 296	48.93 209	78.89 247	77.71 242	60.64 134	49.70 275	72.10 289	27.08 270	83.48 245	54.48 196	62.65 217	76.90 284

Anonymous20231206		59.08 256	57.59 252	63.55 287	68.77 299	32.14 327	80.26 233	79.78 201	50.00 266	49.39 276	72.39 284	26.64 273	78.36 284	33.12 295	57.94 251	80.14 255

Patchmatch-RL test		58.72 259	54.32 271	71.92 208	63.91 321	44.25 272	61.73 317	55.19 329	57.38 200	49.31 277	54.24 331	37.60 178	80.89 261	62.19 134	47.28 304	90.63 71

pmmvs6		59.64 250	57.15 255	67.09 265	66.01 310	36.86 312	80.50 229	78.64 225	45.05 294	49.05 278	73.94 267	27.28 268	86.10 208	43.96 257	49.94 297	78.31 271

v7n		62.50 236	59.27 244	72.20 196	67.25 309	49.83 191	77.87 252	80.12 194	52.50 250	48.80 279	73.07 276	32.10 237	87.90 161	46.83 244	54.92 277	78.86 262

WR-MVS_H		58.91 257	58.04 250	61.54 298	69.07 297	33.83 319	76.91 256	81.99 166	51.40 259	48.17 280	74.67 263	40.23 151	74.15 308	31.78 299	48.10 299	76.64 287

CP-MVSNet		58.54 263	57.57 253	61.46 299	68.50 301	33.96 318	76.90 257	78.60 228	51.67 258	47.83 281	76.60 246	34.99 212	72.79 317	35.45 284	47.58 301	77.64 280

PEN-MVS		58.35 265	57.15 255	61.94 295	67.55 308	34.39 316	77.01 255	78.35 232	51.87 255	47.72 282	76.73 244	33.91 220	73.75 312	34.03 291	47.17 305	77.68 278

PLC	52.38 18	60.89 245	58.97 247	66.68 270	81.77 138	45.70 258	78.96 246	74.04 280	43.66 303	47.63 283	83.19 174	23.52 292	77.78 294	37.47 272	60.46 228	76.55 289
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchMatch-RL		56.66 271	53.75 274	65.37 279	77.91 209	45.28 261	69.78 300	60.38 324	41.35 310	47.57 284	73.73 268	16.83 321	76.91 298	36.99 278	59.21 236	73.92 305

PS-CasMVS		58.12 266	57.03 257	61.37 300	68.24 305	33.80 320	76.73 258	78.01 236	51.20 260	47.54 285	76.20 254	32.85 229	72.76 318	35.17 286	47.37 303	77.55 281

PVSNet_0	57.04 13	61.19 244	57.24 254	73.02 180	77.45 214	50.31 182	79.43 244	77.36 249	63.96 79	47.51 286	72.45 283	25.03 284	83.78 242	52.76 211	19.22 340	84.96 180

Patchmtry		56.56 273	52.95 279	67.42 263	72.53 271	50.59 171	59.05 323	71.72 295	37.86 319	46.92 287	65.86 311	38.94 162	80.06 273	36.94 279	46.72 309	71.60 317

JIA-IIPM		52.33 294	47.77 298	66.03 273	71.20 285	46.92 242	40.00 340	76.48 261	37.10 320	46.73 288	37.02 336	32.96 228	77.88 291	35.97 282	52.45 291	73.29 310

DP-MVS		59.24 252	56.12 261	68.63 255	88.24 27	50.35 179	82.51 192	64.43 318	41.10 311	46.70 289	78.77 219	24.75 286	88.57 139	22.26 327	56.29 265	66.96 326

DTE-MVSNet		57.03 269	55.73 264	60.95 303	65.94 311	32.57 325	75.71 261	77.09 253	51.16 261	46.65 290	76.34 249	32.84 230	73.22 316	30.94 303	44.87 313	77.06 283

OpenMVS_ROB	53.19 17	59.20 253	56.00 262	68.83 250	71.13 286	44.30 271	83.64 161	75.02 272	46.42 286	46.48 291	73.03 277	18.69 314	88.14 154	27.74 315	61.80 222	74.05 304

XVG-ACMP-BASELINE		56.03 277	52.85 280	65.58 275	61.91 327	40.95 298	63.36 311	72.43 290	45.20 293	46.02 292	74.09 265	9.20 337	78.12 286	45.13 251	58.27 244	77.66 279

RPSCF		45.77 303	44.13 304	50.68 317	57.67 335	29.66 330	54.92 329	45.25 339	26.69 334	45.92 293	75.92 257	17.43 320	45.70 343	27.44 316	45.95 311	76.67 286

ppachtmachnet_test		58.56 261	54.34 270	71.24 218	71.42 282	54.74 71	81.84 207	72.27 292	49.02 271	45.86 294	68.99 302	26.27 274	83.30 247	30.12 304	43.23 317	75.69 293

EG-PatchMatch MVS		62.40 239	59.59 240	70.81 226	73.29 261	49.05 205	85.81 103	84.78 118	51.85 256	44.19 295	73.48 274	15.52 327	89.85 102	40.16 267	67.24 180	73.54 308

test_0402		56.45 274	53.03 277	66.69 269	76.78 224	50.31 182	81.76 208	69.61 308	42.79 307	43.88 296	72.13 287	22.82 295	86.46 199	16.57 337	50.94 295	63.31 332

LTVRE_ROB	45.45 19	52.73 291	49.74 292	61.69 297	69.78 294	34.99 314	44.52 334	67.60 313	43.11 306	43.79 297	74.03 266	18.54 315	81.45 257	28.39 313	57.94 251	68.62 323
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
pmmvs-eth3d		55.97 278	52.78 281	65.54 276	61.02 330	46.44 248	75.36 267	67.72 312	49.61 268	43.65 298	67.58 306	21.63 303	77.04 296	44.11 256	44.33 314	73.15 312

test20.03		55.22 281	54.07 272	58.68 308	63.14 324	25.00 338	77.69 253	74.78 273	52.64 248	43.43 299	72.39 284	26.21 275	74.76 306	29.31 306	47.05 307	76.28 291

MSDG		59.44 251	55.14 268	72.32 195	74.69 248	50.71 167	74.39 272	73.58 284	44.44 298	43.40 300	77.52 229	19.45 311	90.87 78	31.31 301	57.49 257	75.38 296

FMVSNet5		58.61 260	56.45 259	65.10 281	77.20 220	39.74 301	74.77 269	77.12 252	50.27 265	43.28 301	67.71 305	26.15 276	76.90 299	36.78 280	54.78 279	78.65 265

LS3D		56.40 275	53.82 273	64.12 284	81.12 157	45.69 259	73.42 277	66.14 314	35.30 328	43.24 302	79.88 208	22.18 300	79.62 278	19.10 335	64.00 201	67.05 325

ACMH+	54.58 15	58.55 262	55.24 265	68.50 258	74.68 249	45.80 257	80.27 232	70.21 306	47.15 280	42.77 303	75.48 259	16.73 323	85.98 212	35.10 288	54.78 279	73.72 306

our_test_3		59.11 255	55.08 269	71.18 221	71.42 282	53.29 111	81.96 202	74.52 274	48.32 273	42.08 304	69.28 301	28.14 260	82.15 252	34.35 290	45.68 312	78.11 275

testgi		54.25 285	52.57 283	59.29 306	62.76 325	21.65 342	72.21 288	70.47 304	53.25 245	41.94 305	77.33 233	14.28 329	77.95 290	29.18 307	51.72 293	78.28 272

ACMH	53.70 16	59.78 249	55.94 263	71.28 217	76.59 225	48.35 224	80.15 237	76.11 263	49.74 267	41.91 306	73.45 275	16.50 324	90.31 92	31.42 300	57.63 256	75.17 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		57.56 267	55.15 267	64.79 283	64.57 319	33.12 321	73.17 279	83.87 139	58.98 169	41.75 307	70.03 297	22.54 296	79.92 274	46.12 249	35.31 327	81.32 240

ambc				62.06 294	53.98 336	29.38 332	35.08 342	79.65 205		41.37 308	59.96 324	6.27 343	82.15 252	35.34 285	38.22 325	74.65 300

ITE_SJBPF				51.84 316	58.03 333	31.94 328		53.57 334	36.67 322	41.32 309	75.23 261	11.17 333	51.57 339	25.81 321	48.04 300	72.02 315

F-COLMAP		55.96 279	53.65 276	62.87 291	72.76 268	42.77 284	74.70 271	70.37 305	40.03 312	41.11 310	79.36 212	17.77 318	73.70 313	32.80 296	53.96 284	72.15 314

PM-MVS		46.92 302	43.76 305	56.41 312	52.18 337	32.26 326	63.21 314	38.18 342	37.99 318	40.78 311	66.20 310	5.09 345	65.42 329	48.19 235	41.99 318	71.54 318

EU-MVSNet		52.63 292	50.72 287	58.37 309	62.69 326	28.13 336	72.60 282	75.97 264	30.94 330	40.76 312	72.11 288	20.16 309	70.80 322	35.11 287	46.11 310	76.19 292

testing_2		63.60 226	59.86 239	74.82 143	61.87 328	52.39 135	73.06 280	82.76 159	61.49 120	39.96 313	67.39 307	21.06 306	88.34 146	67.07 100	64.10 199	83.72 202

OurMVSNet-221017-0		52.39 293	48.73 294	63.35 289	65.21 315	38.42 306	68.54 304	64.95 316	38.19 316	39.57 314	71.43 291	13.23 331	79.92 274	37.16 274	40.32 322	71.72 316

K. test v3		54.04 286	49.42 293	67.92 260	68.55 300	42.57 288	75.51 265	63.07 321	52.07 252	39.21 315	64.59 315	19.34 312	82.21 251	37.11 276	25.31 337	78.97 261

SixPastTwentyTwo		54.37 283	50.10 289	67.21 264	70.70 288	41.46 294	74.73 270	64.69 317	47.56 277	39.12 316	69.49 298	18.49 316	84.69 235	31.87 298	34.20 332	75.48 295

UnsupCasMVSNet_bld		53.86 287	50.53 288	63.84 285	63.52 323	34.75 315	71.38 292	81.92 169	46.53 283	38.95 317	57.93 328	20.55 308	80.20 272	39.91 268	34.09 333	76.57 288

Patchmatch-test		53.33 290	48.17 295	68.81 251	73.31 260	42.38 289	42.98 336	58.23 326	32.53 329	38.79 318	70.77 293	39.66 158	73.51 314	25.18 322	52.06 292	90.55 72

LCM-MVSNet-Re		58.82 258	56.54 258	65.68 274	79.31 181	29.09 334	61.39 320	45.79 337	60.73 132	37.65 319	72.47 282	31.42 245	81.08 260	49.66 225	70.41 160	86.87 146

CMPMVS	40.41 21	55.34 280	52.64 282	63.46 288	60.88 331	43.84 275	61.58 319	71.06 302	30.43 331	36.33 320	74.63 264	24.14 288	75.44 303	48.05 236	66.62 183	71.12 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
COLMAP_ROB	43.60 20	50.90 296	48.05 296	59.47 305	67.81 307	40.57 300	71.25 293	62.72 323	36.49 323	36.19 321	73.51 273	13.48 330	73.92 311	20.71 331	50.26 296	63.92 331
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
lessismore_v0				67.98 259	64.76 318	41.25 295		45.75 338		36.03 322	65.63 313	19.29 313	84.11 238	35.67 283	21.24 339	78.59 266

USDC		54.36 284	51.23 286	63.76 286	64.29 320	37.71 309	62.84 316	73.48 288	56.85 205	35.47 323	71.94 290	9.23 336	78.43 283	38.43 271	48.57 298	75.13 298

N_pmnet		41.25 305	39.77 307	45.66 321	68.50 301	0.82 354	72.51 284	0.38 355	35.61 325	35.26 324	61.51 320	20.07 310	67.74 327	23.51 326	40.63 320	68.42 324

DSMNet-mixed		38.35 307	35.36 309	47.33 320	48.11 341	14.91 348	37.87 341	36.60 344	19.18 339	34.37 325	59.56 326	15.53 326	53.01 338	20.14 333	46.89 308	74.07 303

pmmvs3		45.53 304	41.55 306	57.44 311	48.97 340	39.68 302	70.06 297	57.66 327	28.32 333	34.06 326	57.29 329	8.50 338	66.85 328	34.86 289	34.26 331	65.80 328

new-patchmatchnet		48.21 299	46.55 300	53.18 315	57.73 334	18.19 346	70.24 296	71.02 303	45.70 288	33.70 327	60.23 323	18.00 317	69.86 325	27.97 314	34.35 330	71.49 319

MVS-HIRNet		49.01 298	44.71 301	61.92 296	76.06 231	46.61 246	63.23 313	54.90 330	24.77 335	33.56 328	36.60 337	21.28 305	75.88 302	29.49 305	62.54 218	63.26 333

AllTest		47.32 301	44.66 302	55.32 313	65.08 316	37.50 310	62.96 315	54.25 332	35.45 326	33.42 329	72.82 278	9.98 334	59.33 332	24.13 324	43.84 315	69.13 321

TestCases				55.32 313	65.08 316	37.50 310		54.25 332	35.45 326	33.42 329	72.82 278	9.98 334	59.33 332	24.13 324	43.84 315	69.13 321

MIMVSNet1		50.35 297	47.81 297	57.96 310	61.53 329	27.80 337	67.40 305	74.06 279	43.25 305	33.31 331	65.38 314	16.03 325	71.34 321	21.80 328	47.55 302	74.75 299

YYNet1		53.82 288	49.96 290	65.41 278	70.09 293	48.95 207	72.30 286	71.66 297	44.25 299	31.89 332	63.07 318	23.73 290	73.95 310	33.26 293	39.40 323	73.34 309

MDA-MVSNet_test_wron		53.82 288	49.95 291	65.43 277	70.13 292	49.05 205	72.30 286	71.65 298	44.23 300	31.85 333	63.13 317	23.68 291	74.01 309	33.25 294	39.35 324	73.23 311

TDRefinement		40.91 306	38.37 308	48.55 319	50.45 338	33.03 323	58.98 324	50.97 335	28.50 332	29.89 334	67.39 307	6.21 344	54.51 336	17.67 336	35.25 328	58.11 334

TinyColmap		48.15 300	44.49 303	59.13 307	65.73 312	38.04 307	63.34 312	62.86 322	38.78 314	29.48 335	67.23 309	6.46 342	73.30 315	24.59 323	41.90 319	66.04 327

LF4IMVS		33.04 311	32.55 311	34.52 327	40.96 343	22.03 341	44.45 335	35.62 345	20.42 337	28.12 336	62.35 319	5.03 346	31.88 348	21.61 330	34.42 329	49.63 337

MDA-MVSNet-bldmvs		51.56 295	47.75 299	63.00 290	71.60 280	47.32 238	69.70 301	72.12 293	43.81 302	27.65 337	63.38 316	21.97 302	75.96 301	27.30 317	32.19 334	65.70 329

new_pmnet		33.56 310	31.89 312	38.59 324	49.01 339	20.42 343	51.01 330	37.92 343	20.58 336	23.45 338	46.79 333	6.66 341	49.28 341	20.00 334	31.57 336	46.09 339

FPMVS		35.40 308	33.67 310	40.57 323	46.34 342	28.74 335	41.05 338	57.05 328	20.37 338	22.27 339	53.38 332	6.87 340	44.94 344	8.62 341	47.11 306	48.01 338

LCM-MVSNet		28.07 312	23.85 316	40.71 322	27.46 350	18.93 345	30.82 343	46.19 336	12.76 343	16.40 340	34.70 340	1.90 351	48.69 342	20.25 332	24.22 338	54.51 335

ANet_high		34.39 309	29.59 313	48.78 318	30.34 348	22.28 340	55.53 326	63.79 320	38.11 317	15.47 341	36.56 338	6.94 339	59.98 331	13.93 339	5.64 348	64.08 330

PMMVS2		26.71 314	22.98 317	37.87 325	36.89 345	8.51 352	42.51 337	29.32 349	19.09 340	13.01 342	37.54 335	2.23 349	53.11 337	14.54 338	11.71 341	51.99 336

tmp_tt		9.44 320	10.68 322	5.73 334	2.49 353	4.21 353	10.48 347	18.04 351	0.34 349	12.59 343	20.49 343	11.39 332	7.03 351	13.84 340	6.46 347	5.95 346

PMVS	19.57 22	25.07 315	22.43 318	32.99 328	23.12 351	22.98 339	40.98 339	35.19 346	15.99 341	11.95 344	35.87 339	1.47 353	49.29 340	5.41 346	31.90 335	26.70 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 313	24.26 315	37.12 326	60.55 332	29.17 333	11.68 346	60.00 325	14.18 342	10.52 345	15.12 346	2.20 350	63.01 330	8.39 342	35.65 326	19.18 342
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 319	13.39 321	29.16 329	28.43 349	19.72 344	13.73 345	23.63 350	7.23 347	7.96 346	21.41 342	0.80 354	36.08 347	6.97 343	10.39 342	31.69 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 332	21.34 352	8.99 351		10.02 353	10.59 345	7.53 347	30.55 341	1.82 352	14.55 349	6.83 344	7.52 344	15.75 343

E-PMN		19.16 316	18.40 319	21.44 330	36.19 346	13.63 349	47.59 331	30.89 347	10.73 344	5.91 348	16.59 344	3.66 348	39.77 345	5.95 345	8.14 343	10.92 344

EMVS		18.42 317	17.66 320	20.71 331	34.13 347	12.64 350	46.94 332	29.94 348	10.46 346	5.58 349	14.93 347	4.23 347	38.83 346	5.24 347	7.51 345	10.67 345

wuyk23d		9.11 321	8.77 324	10.15 333	40.18 344	16.76 347	20.28 344	1.01 354	2.58 348	2.66 350	0.98 350	0.23 355	12.49 350	4.08 348	6.90 346	1.19 347

testmvs		6.14 323	8.18 325	0.01 335	0.01 354	0.00 356	73.40 278	0.00 356	0.00 350	0.02 351	0.15 351	0.00 356	0.00 352	0.02 349	0.00 349	0.02 348

test123		6.01 324	8.01 326	0.01 335	0.00 355	0.01 355	71.93 290	0.00 356	0.00 350	0.02 351	0.11 352	0.00 356	0.00 352	0.02 349	0.00 349	0.02 348

uanet_test		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

cdsmvs_eth3d_5k		18.33 318	24.44 314	0.00 337	0.00 355	0.00 356	0.00 348	89.40 17	0.00 350	0.00 353	92.02 36	38.55 166	0.00 352	0.00 351	0.00 349	0.00 350

pcd_1.5k_mvsjas		3.15 325	4.20 327	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	37.77 172	0.00 352	0.00 351	0.00 349	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

ab-mvs-re		7.68 322	10.24 323	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	92.12 33	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 355	0.00 356	0.00 348	0.00 356	0.00 350	0.00 353	0.00 353	0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

OPU-MVS				81.71 12	92.05 3	55.97 41	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

save fliter					85.35 62	56.34 35	89.31 37	81.46 176	61.55 116

test_0728_SECOND				82.20 8	89.50 11	57.73 12	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

GSMVS																88.13 126

test_part1				0.00 337		0.00 356	0.00 348	88.42 45				0.00 356	0.00 352	0.00 351	0.00 349	0.00 350

sam_mvs1												38.86 164				88.13 126

sam_mvs												35.99 205

MTGPA								81.31 179

test_post1							70.84 295				14.72 348	34.33 216	83.86 239	48.80 230

test_post											16.22 345	37.52 180	84.72 234

patchmatchnet-post											59.74 325	38.41 167	79.91 276

MTMP							87.27 74	15.34 352

gm-plane-assit					83.24 104	54.21 84			70.91 12		88.23 113		95.25 12	66.37 104

test9_res														78.72 27	85.44 40	91.39 52

agg_prior2														75.65 47	85.11 44	91.01 63

test_prior4						56.39 34	87.15 77

test_prior				78.39 65	86.35 43	54.91 68		85.45 92					89.70 107			90.55 72

新几何2							81.61 213

旧先验1					81.57 146	47.48 236		71.83 294			88.66 105	36.94 191			78.34 98	88.67 115

无先验							85.19 121	78.00 237	49.08 270				85.13 228	52.78 208		87.45 139

原ACMM2							83.77 159

testdata2													77.81 293	45.64 250

segment_acmp												44.97 98

testdata1							77.55 254		64.14 76

plane_prior7					77.95 206	48.46 223

plane_prior6					78.42 201	49.39 200						36.04 203

plane_prior5								82.59 160					88.30 150	65.46 114	72.34 148	84.49 184

plane_prior4											83.28 172

plane_prior2							85.76 105		63.60 86

plane_prior1					78.31 203

plane_prior						49.57 194	87.43 68		64.57 72						72.84 143

n2								0.00 356

nn								0.00 356

door-mid								41.31 341

test11								84.25 129

door								43.27 340

HQP5-MVS						51.56 153

BP-MVS														66.70 101

HQP3-MVS								83.68 141							73.12 139

HQP2-MVS												37.35 183

NP-MVS					78.76 189	50.43 175					85.12 151

ACMMP++_ref															63.20 211

ACMMP++															59.38 235

Test By Simon												39.38 159

High-resolution multi-view benchmark